Skip to content

Move PyGAD logo to top of docs #367

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
ahmedfgad merged 2 commits into from
Jun 14, 2026
+4 −4
Merged

Move PyGAD logo to top of docs #367

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
a597d92
Merge pull request #366 from ahmedfgad/master
ahmedfgad Jun 11, 2026
30da908
Move PyGAD logo at the beginning
ahmedfgad Jun 14, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
8 changes: 4 additions & 4 deletions docs/source/index.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,10 @@

[PyGAD](https://github.com/ahmedfgad/GeneticAlgorithmPython) is an open-source Python library for building the genetic algorithm and optimizing machine learning algorithms. It works with [Keras](https://keras.io) and [PyTorch](https://pytorch.org).

![PYGAD-LOGO](https://user-images.githubusercontent.com/16560492/101267295-c74c0180-375f-11eb-9ad0-f8e37bd796ce.png)

*Logo designed by [Asmaa Kabil](https://www.linkedin.com/in/asmaa-kabil-9901b7b6)*

> Try [Vilvik](https://vilvik.com), a free cloud-based tool powered by PyGAD. It makes optimization easier by reducing or removing the need for coding, and it shows helpful visualizations.

> Run PyGAD in the cloud with [Vilvik](https://vilvik.com): push your PyGAD problem to Vilvik, let it run in the cloud, and get the results back.
Expand All @@ -16,10 +20,6 @@ Push your PyGAD problem to [Vilvik](https://vilvik.com) and run it in the cloud.

[PyGAD](https://github.com/ahmedfgad/GeneticAlgorithmPython) supports different types of crossover, mutation, and parent selection operators. It lets you optimize many types of problems with the genetic algorithm by writing your own fitness function. It works with both single-objective and multi-objective optimization problems.

![PYGAD-LOGO](https://user-images.githubusercontent.com/16560492/101267295-c74c0180-375f-11eb-9ad0-f8e37bd796ce.png)

*Logo designed by [Asmaa Kabil](https://www.linkedin.com/in/asmaa-kabil-9901b7b6)*

Besides building the genetic algorithm, PyGAD builds and optimizes machine learning algorithms. At the moment, [PyGAD](https://pypi.org/project/pygad) supports building and training (using the genetic algorithm) artificial neural networks for classification problems.

The library is under active development, and new features are added often. Please contact us if you want a feature to be supported.
Expand Down
Loading